Biomedical signals “suffer” from common problem of poor signal-to-noise ratio (SNR). This problem becomes acute whenever it comes to measuring ElecroEncyphalogram (EEG) and ElecroMyogram (EMG) signals, since the magnitude of such signals is generally in the μVolt range. The signals picked-up by corresponding detectors, which are attached to different portions of the patient body, are forwarded to the analyzing system by wires, which may add significant noise to the already weak signal. In addition, the characteristics of the wires may change according to the ambient conditions, a problem that must be overcome by utilizing special hardware and/or software resources. Another problem arises whenever the patient changes his relative position. Under such circumstances, the analyzing system may interpret false signals as valid pathological events. Therefore, it would be advantageous to have an analyzing system that is capable of recognizing and disregarding such false events.
In some conventional systems, wherein several physiological parameters of a patient are examined (e.g., EEG, ECG and EMG and respiratory), each parameter is analyzed regardless of other physiological parameters. However, in some cases, several parameters could be strongly interrelated. For example, an ‘irregular’ heartbeat rate (HBR) of a patient might be interpreted by a clinician as pathological HBR if analyzed regardless of other factors. However, it might be interpreted as proper HBR if correlated with brain waves indicating to the analyzing system that the patient is dreaming. Currently, in such systems a clinician has to ‘manually’ correlate the various raw measurements, which are related to different physiological aspects, and reach conclusions therefrom.
Therefore, it would be advantageous to have an analysis system, which would allow measuring several types of physiological signals, and could introduce to the clinician already-correlated data, i.e., a data that is already correlated with (other) relevant physiological parameters so as to eliminate artifacts and other types of noises/interference. Such a system would introduce to the clinician relevant, more accurate and well-characterized selected physiological events, and save the clinician analyzing/interpretation time. For example, it would be advantageous to combine abdominal effort, Oxygen saturation, patient's, movement or arousals, and sleep stages in order to reach a decision regarding apneas.
Different patients are likely to introduce different ‘normal’ physiological activities due to the variance of, e.g., heartbeat morphology (see FIGS. 1a and 1b). Therefore, it would be advantageous to have a system that would be capable of automatically adapting itself to the specific heartbeat morphology of a person being monitored, because such system would allow more accurate detection of physiological abnormalities.
Other conventional systems, generally called ‘semi-automatic’ systems, are capable of sampling, processing and analyzing electro-physiological signals. However, these systems configuration allows them to only analyze physiological signals between two ‘rigid’ boundaries, in time and/or in magnitude, which must be predetermined by the clinician. The two predetermined boundaries are selected on a random base, and, therefore, processing and analyzing the raw data contained within these boundaries usually does not yield satisfactory results. In some cases, the results could be even meaningless. Therefore, the clinician must iterate a sequence of operations, on a ‘trial and error’ basis, until meaningful results are obtained. Therefore, it would be advantageous to have a system that would be capable of ‘self-tuning’ to the required data portion, without needing to depend on specific or predetermined boundaries of any type, between which the raw data is to be processed and analyzed.
There are several common diseases associated with heart conditions, which are characterized by the corresponding irregularities in the heart behavior between distinguishable points in a heartbeat cycle (commonly known as points ‘P’, ‘Q’, ‘R’, ‘S’ and ‘T’). FIGS. 1(a) and 1(b) illustrate typical heartbeats, in which the ‘P’, ‘Q’, ‘R’, ‘S’ and ‘T’ points are marked. Conventional systems utilize algorithms of rather limited flexibility (e.g., using ‘rigid’ predetermined boundaries). Therefore, they offer solutions that focus on limited aspects of heart analysis. For example, one system focuses on analyzing irregularities between points ‘S’ and ‘T’, another system on irregularities between points ‘P’ and ‘R’ etc. It would be advantageous to have a system that would be capable of isolating and classifying essentially every type of irregularity associated with a heart condition, regardless of the deformation (see FIGS. 1(b), 1(d) and 1(e)) and relative location of such irregularity.
Conventional systems usually display measurement results graphically on paper stripe or on electronic display, after which they are reviewed and estimated by a clinician or therapist. In some cases, these results are stored in an electronic storage media. Sometimes, the physiological activity of a patient is monitored for several hours but the clinician/therapist is interested only in small portions of the recorded data, which are associated with abnormal physiological activity of the patient. In conventional systems, the rest, and sometimes most, of the recorded data is useless because it is either too corrupted or it does not contribute anything to the assessment of the patient physiological condition, and is, therefore, dumped away (whenever a paper stripe is used), or a data storage place is unnecessarily consumed (whenever an electronic storage media is used), which leads to wasted resources. Other applications require collecting physiological signals of specific patient for several hours in order to allow good assessment of the patient physiological condition. In this case, the clinician must review and analyze an enormous amount of data, which is usually a burdensome process. The analysis lasts a long period of time and changes with technician tiredness. It would be advantageous to have a system, which is capable of performing the analysis with the same reliability.
In connection with sleep disorders, most current sleep (polysomnographic) studies are carried out in Sleep Centers, where patients need to spend at least one night in a hospital or private laboratory. The fact that patients need to sleep in the laboratory facilities presents significant difficulties. First, with the growing awareness to sleep disorders, waiting lists are growing, and delays of months and years are common. Second, the sleep study is expensive and thus the availability of sleep monitoring is more difficult. But more important fact is that the patient's sleep is affected whenever studied in a non-familiar laboratory setting. For example, sleep efficiency and patterns are badly affected due to the phenomena generally known as the “first night” effect. Therefore, in order to achieve a genuine sleep behavior of a patient, it is necessary to study the patient (especially for clinical research) for at least two consecutive nights, a fact that increases the expenses.
The advantages of conducting sleep studies at the patient home include increased flexibility of scheduling, less disruption to the routine of the child and family, and the ability to study the patient, especially a child, in his, or her, natural environment. While this has not yet been documented, it seems that home studies will be more cost-efficient than laboratory polysomnography, as a hospital admission is not required. Moreover, because such studies usually do not require the attendance of technicians, technician time is substantially decreased, or it could even be evaded. These factors become even more important when more than one night of recording is required.
More and more Sleep Centers are trying to screen their patients by performing partial ambulatory sleep studies at home. Although this is a growing field in sleep medicine, lacking automatic signal processing and monitoring greatly complicates the unattended tests.
A home video and partial cardiovascular electrode are successfully employed in Montreal, using a customized computer program. The possibility of using a simple, non-invasive data acquisition at home, monitored via a computer/Internet, on line, may facilitate home sleep monitoring for millions of patients. A patient will be able to remain in his own bedroom, for as long as required, while being continuously monitored and the data inspected on-line by a technician, enabling him to handle a problematic signal from the center or even call the patient at his home, to prevent waste of time due to disrupted data acquisition. In addition, the data will be analyzed on-line, in real time and automatically by the computer, thereby saving a lot of technician working hours and making the results readily available for both patient and his clinician.
Conventional systems produced by companies like Compumedics, Flaga, Respironoics, etc., are capable of automatically analyzing the mentioned signals. However, in order to achieve reliable results, an array of thresholds should be provided and adjusted manually in order for these systems to overcome the problem of different patients having different inherent signal variations and, thereby, to manually converge to the patient self-parameters.
There are major three frequency bands which characterize the EEG signal during sleep. The statistical characteristics of the frequency content of each group statistically differ from one person to another. Therefore, it is essential that a sleep monitoring system would have an adaptive mechanism, which would allow it to reach correct decisions regarding the interpretation of each frequency group on individual basis.
All of the methods described above have not yet provided satisfactory solutions to the problem of automatically adapting and optimizing the biomedical analyzing system to a patient being monitored. Additionally, the prior art methods have not yet provided satisfactory solutions to the problem of integrating analysis of ECG, EMG, EEG and respiratory signals.